code: def setup_text_processor(self, processor: ABCProcessor) -> None:
      self._text_processor = processor
      self.build_embedding_model(vocab_size=processor.vocab_size)
      self._text_processor.segment = self.segment
      if self.vocab2idx:
          self._text_processor.vocab2idx = self.vocab2idx
          self._text_processor.idx2vocab = dict(
              [(v, k) for k, v in self.vocab2idx.items()]
          )
  
file_path: Kashgari/kashgari/embeddings/abc_embedding.py
imports: import json
  from typing import Dict, List, Any, Optional, Union
  import numpy
  import tensorflow
  import tqdm
  import kashgari
  from kashgari.generators import CorpusGenerator
  from kashgari.logger import logger
  from kashgari.processors import ABCProcessor
label_desc: Use image node_id 4 for calling the ABCEmbedding obj's underlying member method code with example usage: obj.setup_text_processor(processor) without return types